基于RankClus算法的机场流程日志活动挖掘

流程挖掘技术可以提取机场流程日志中的有用信息用于流程分析。但机场流程日志处于细节化的低抽象层次，不符合分析者的预期。对机场流程日志挖掘得到的流程模型呈现意面状的复杂结构，流程模型的含义难于理解。解决该问题的一种方法是通过活动挖掘，将低抽象层次活动聚类为流程模型中表征高抽象层次活动的活动类簇。为此提出了一种基于RankClus算法的活动挖掘方法，将机场流程日志的活动聚类与活动排序评分计算相结合，从而构建更易理解的活动聚类流程模型。实验结果表明，RankClus活动聚类流程模型的日志回放一致性与原生日志流程模型大致相当，但在结构复杂度上要显著低于原生日志流程模型。

Activity Mining for Airport Event Logs Based on RankClus Algorithm

Process mining is a technology which can extract non-trivial and useful information from airport event logs. However, the airport event logs are always on a detailed level of abstraction, which may not be in line with the expected abstract level of an analyst. Process models generated by these event logs are always spaghetti-like and too hard to comprehend. An approach to overcome this issue is to group low-level events into clusters, which represent the execution of a higher-level activity in the process model. Therefore, this paper presents a new activity mining method which is based on RankClus algorithm to generate activity clusters integrated with ranking. On this basis, the activity-clustered model which is easier to comprehend can be constructed. The experiment results show that this activity-clustered model, which shares a similar level of conformance with the meta model, is significantly less complex.